code: def test_predict_meta_override():
      X = pd.DataFrame({"c_0": [1, 2, 3, 4]})
      y = np.array([1, 2, 3, 4])
  
      base = CategoricalNB()
      base.fit(pd.DataFrame(X), y)
  
      dd_X = dd.from_pandas(X, npartitions=2)
      dd_X._meta = pd.DataFrame({"c_0": [5]})
  
      # Failure when not proving predict_meta
      # because of value dependent model
      wrap = ParallelPostFit(base)
      with pytest.raises(ValueError):
          wrap.predict(dd_X)
  
      # Success when providing meta over-ride
      wrap = ParallelPostFit(base, predict_meta=np.array([1]))
      result = wrap.predict(dd_X)
      expected = base.predict(X)
      assert_eq_ar(result, expected)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 4 for calling a global function with example usage: test_predict_meta_override() without return types